Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things
The measurement of the concentration of suspended sediment in a water body is a very important content in the observation of hydrological elements, and it is also one of the important parameters for calculating the sediment resuspension flux. In order to accurately predict the distribution of lake s...
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doaj-82bb4eb6dbde4c81a8c27370993e7cce2021-06-22T23:00:36ZengIEEEIEEE Access2169-35362021-01-019878498785610.1109/ACCESS.2021.30893679454460Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of ThingsMing Zeng0Jun Peng1https://orcid.org/0000-0003-0501-2679Ling Jiang2Jingjuan Feng3Jiangxi Yangtze River Economic Zone Research Institute, Jiujiang University, Jiujiang, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaThe measurement of the concentration of suspended sediment in a water body is a very important content in the observation of hydrological elements, and it is also one of the important parameters for calculating the sediment resuspension flux. In order to accurately predict the distribution of lake sediment, this paper uses satellite remote sensing data to invert the suspended sediment concentration. The key to the quantitative inversion is the atmospheric correction and the suspended sediment concentration inversion algorithm. In this paper, satellite remote sensing technology and Internet of Things technology are combined to establish a new type of lake suspended sediment concentration distribution model. First of all, this paper combines the results of satellite remote sensing inversion and the results of on-site water sample inspections of the Internet of Things to obtain the original hydrological data of suspended sediment in the lake. Secondly, this paper combines ADAM with deep learning technology to simulate the lake flow field and predict the dynamic process of suspended sediment pollution under different conditions. Finally, through experimental simulation and field sampling experiments, the validity of the lake suspended sediment concentration model established in this paper is verified. This model can provide assistance for relevant agencies to grasp the temporal and spatial distribution of suspended sediment concentration in regional lakes in a comprehensive and timely manner, and can obtain the overall characteristics of the study area and the impact of humanistic engineering construction.https://ieeexplore.ieee.org/document/9454460/Lake sediment concentrationInternet of Thingstemporal and spatial distributionsatellite remote sensingADAM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ming Zeng Jun Peng Ling Jiang Jingjuan Feng |
spellingShingle |
Ming Zeng Jun Peng Ling Jiang Jingjuan Feng Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things IEEE Access Lake sediment concentration Internet of Things temporal and spatial distribution satellite remote sensing ADAM |
author_facet |
Ming Zeng Jun Peng Ling Jiang Jingjuan Feng |
author_sort |
Ming Zeng |
title |
Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things |
title_short |
Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things |
title_full |
Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things |
title_fullStr |
Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things |
title_full_unstemmed |
Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things |
title_sort |
temporal and spatial distribution of suspended sediment concentration in lakes based on satellite remote sensing and internet of things |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
The measurement of the concentration of suspended sediment in a water body is a very important content in the observation of hydrological elements, and it is also one of the important parameters for calculating the sediment resuspension flux. In order to accurately predict the distribution of lake sediment, this paper uses satellite remote sensing data to invert the suspended sediment concentration. The key to the quantitative inversion is the atmospheric correction and the suspended sediment concentration inversion algorithm. In this paper, satellite remote sensing technology and Internet of Things technology are combined to establish a new type of lake suspended sediment concentration distribution model. First of all, this paper combines the results of satellite remote sensing inversion and the results of on-site water sample inspections of the Internet of Things to obtain the original hydrological data of suspended sediment in the lake. Secondly, this paper combines ADAM with deep learning technology to simulate the lake flow field and predict the dynamic process of suspended sediment pollution under different conditions. Finally, through experimental simulation and field sampling experiments, the validity of the lake suspended sediment concentration model established in this paper is verified. This model can provide assistance for relevant agencies to grasp the temporal and spatial distribution of suspended sediment concentration in regional lakes in a comprehensive and timely manner, and can obtain the overall characteristics of the study area and the impact of humanistic engineering construction. |
topic |
Lake sediment concentration Internet of Things temporal and spatial distribution satellite remote sensing ADAM |
url |
https://ieeexplore.ieee.org/document/9454460/ |
work_keys_str_mv |
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